## 1. Welfare vs. Income in 2014 ## Source details **Canonical URL:** [1. Welfare vs. Income in 2014](https://www.imf.org/-/media/files/publications/wp/2017/wp17271.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2017/wp17271.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2017/wp17271.pdf.json) --- ### Introduction and motivation - GDP (or GNI) per capita is the primary metric for living standard comparisons but omits dimensions that affect welfare, including: - the distribution of income and wealth, - household production, - use and destruction of the natural environment, - inequality in economic opportunity, - governance, security, health and longevity. - Alternative summary measures exist (HDI, IDI, Multidimensional Poverty Index) but have been criticized for lack of theoretical foundations (Fleurbaey, 2009; Ravallion, 2012). - Jones and Klenow (2010, 2016) propose a theoretically grounded summary measure of welfare based on consumption equivalents that combine consumption, leisure, inequality and mortality using expected utility (in the tradition of Lucas, 1987). ### Methodology and extensions used in this chapter - Updated index calculation for the years 2007 and 2014 using a broader set of countries (Jones and Klenow originally for 1980 and 2007). - The Jones and Klenow approach: compute a consumption equivalent—what proportion of U.S. consumption, given U.S. valuations of leisure, mortality and inequality, yields the same expected lifetime utility as in another country. - Extension introduced in this paper: - Incorporate environmental externalities and sustainable use of natural resources via a national-level public environmental “bad” (pollution). - The public bad reduces representative household welfare through three channels: 1. increasing mortality/morbidity due to environmental health effects, lowering expected consumption, 2. reducing household consumption in proportion to a tax on a global environmental externality (greenhouse gas emissions, GHG), 3. lowering consumption in proportion to the level of unsustainable exploitation of natural resources. ### Coverage - Welfare index calculations presented for 152 countries in 2014 to examine patterns of welfare across countries. ### Key empirical findings for 2014 - Welfare is highly correlated with GDP per capita, but significant deviations exist, mostly related to differences in life expectancy. - Consumption, leisure and inequality also affect the index in particular cases. - For Asian countries: - Welfare is generally related to differences in life expectancy. - Consumption, leisure and inequality have negative effects on the index because Asian households tend to consume less, work more, and face higher inequality than in the U.S. - Correlation and dispersion statistics (2014): - Correlation coefficient of welfare and GDP per capita: 0.95. - Mean absolute deviation from unity of the ratio (lambda to GDP per capita): about 15 percent. - Population-weighted averages (2014): overall population weighted average welfare is 22.6 percent of the US level; weighted average per capita income is 28.0 percent of the US level. - Regional population-weighted snapshots (2014): - Europe aggregate: welfare 58.0 percent of US level; per capita income 56.0 percent. - Western Europe: welfare 85.2 percent; income 73.8 percent. - Sub-Saharan Africa: welfare 4.2 percent; income 7.8 percent. - Dominant drivers: - Life expectancy is the dominant driver of deviations between welfare and income: high-income countries often have higher welfare relative to income due to higher life expectancy, more leisure and less inequality. - Poorer countries typically show welfare below relative income, driven mainly by lower life expectancy and higher inequality. - Consumption can dominate in specific cases (poorer countries and small island economies that consume beyond income due to grants). ### Growth and convergence, 2007–2014 - Purpose: assess changes between 2007 and 2014, a period reflecting the impact of the global financial crisis, and measure convergence in the welfare index and income. - Level changes (population-weighted, 2007–2014): - Welfare convergence toward the US level: 2.5 percentage points. - Income convergence toward the US level: 4.6 percentage points. - Aggregate growth (population-weighted average annual rates, 2007–2014): - Average, unweighted: Welfare 3.92; Income 2.92. - Average, pop-weighted: Welfare 4.87; Income 4.64. - Aggregate statement: both welfare and (PPP-adjusted) per capita income grew rapidly at between 4 ½ and 5 percent per annum over the period (driven by large weight of fast-growing countries such as China and Indonesia). - Regional growth highlights (population-weighted average annual rates): - Europe: Welfare 4.18; Income 2.47. - Western Europe: Welfare 2.33; Income 0.88. - Eastern Europe: Welfare 6.16; Income 4.16. - North America: Welfare 1.33; Income 0.42. - Latin America: Welfare 4.48; Income 3.55. - Asia: Welfare 5.78; Income 6.21. - Sub-Saharan Africa: Welfare 3.77; Income 3.60. - Country examples (average annual growth rates, decomposition): - United States: Welfare 1.12; Income 0.19. - China: Welfare 4.86; Income 6.54 (C/Y contribution -1.68). - Indonesia: Welfare 6.01; Income 9.52 (C/Y contribution -3.51; inequality contribution -1.27). - Japan: Welfare 1.92; Income -0.35. - Hong Kong: Welfare 3.64; Income -1.99 (C/Y contribution 5.63). - Synthesis: income per capita converged across countries from 2007 to 2014; welfare converged even more than income in aggregate statistics, but population-weighted results show income converging about twice as fast as welfare in terms of levels toward the US. Life-expectancy improvements were the main contribution to welfare convergence and growth; consumption gains were small or negative on average; leisure and inequality changed little. ### Environmental effects on welfare (2012 and 2014 data / extensions) - Implementation approach (2012): - Introduce DALYs (age-standardized disability adjusted life years) attributable to environmental factors to disaggregate life expectancy into adjusted life expectancy and years of life lost to environmental pollution. - Introduce a CO2 (GHG) emissions welfare cost: set social cost of each ton at US$30, converted to PPP constant dollars; define ݎ(݀) = log(1 - ߬), with ߬ = (social cost of CO2 emissions in country i per unit of consumption). - Adjust consumption for sustainability using Adjusted Net Savings (ANS) gap: when ANS negative, consumption is reduced by the size of the negative net savings gap. - DALYs / life-expectancy effects: - Countries with highest DALYs relative to the US (largest welfare loss) are mostly poor African countries (Sierra Leone, Chad, Swaziland, Cote d’Ivoire). - Overall magnitude: effect of years of life lost to environmental pollution is small — on the order of one half of one percent of flow utility for the most affected countries (DALYs around a third of a year per capita in the worst cases). - Welfare loss from global GHG emissions (US$30/ton): - Valuing GHG emissions at US$30/ton and charging as a consumption-equivalent tax reduces recorded welfare in countries with large emissions per unit of consumption. - Examples of most negatively affected: Bolivia, Lao PDR, Zambia — inclusion of GHG effects reduces recorded welfare by 5 percent, 3.3 percent, and 1.7 percentage points of the US level respectively for these countries. - Mechanism: (i) indirect life-expectancy effect (tax reduces flow consumption and thus reduces consumption-weighted life-expectancy shortfall), and (ii) direct welfare loss from the tax on GHG emissions (usually dominates). - Some countries are positively affected because their emissions per unit of consumption are lower than the US (examples include advanced European countries, Japan, Hong Kong, Costa Rica); positive effects range up to about 0.01 to 0.10 of a percentage point of US welfare level for some countries. - Adjusted Net Savings (ANS) / sustainability: - Countries with negative ANS in 2012 (ANS gap, percent of consumption) include: - Guinea (consumption per capita 1.85; ANS gap -44.68) - Oman (35.24; -26.10) - Liberia (1.93; -21.07) - Togo (2.12; -20.48) - Greece (50.61; -7.43) - Lebanon (33.00; -5.62) - Laos (8.23; -4.58) - Madagascar (2.83; -3.68) - Belize (16.48; -3.53) - Cameroon (5.42; -2.17) - Malawi (2.68; -1.18) - Tunisia (20.67; -0.82) - Barbados (30.93; -0.35) - Portugal (52.30; -0.21) - Welfare effect: inclusion of ANS gap reduces consumption (to be sustainable), producing two counteracting effects (reduced consumption-weighted life-expectancy shortfall vs. direct reduction in consumption). The direct consumption effect typically dominates. - Example magnitudes: - Guinea’s welfare index declines proportionally by 35 percent (from 2.5 to 1.6 percentage points of the US level). - Oman declines by 25 percent (11 percentage points of the US level). - Portugal declines by 0.6 percent (0.4 percentage points of the US level). - Overall environmental findings: - DALY-adjusted life-expectancy effects on welfare are small in magnitude. - GHG emissions valued at US$30/ton can reduce welfare materially where emissions per unit of consumption are high (declines up to several percentage points of US welfare level for the worst-affected countries). - Negative ANS (unsustainable consumption) causes nontrivial welfare declines for affected countries. ### Policy-relevant implications and suggested extensions - Policy implications: - Rebalancing growth toward domestic consumption (away from investment/exports) in countries with low C/Y can raise welfare (explicitly highlighted for China, Indonesia, and several ASEAN countries). - Accounting for global GHG costs and sustainability (ANS) alters welfare rankings and highlights the welfare importance of natural-resource-intensive consumption in some countries. - Data and methodological caveats / suggested extensions: - Need to adjust labor input measures to take into account involuntary unemployment (especially in crisis countries) and informal labor in lower-income countries — current Penn Tables hours-worked data do not capture these effects fully. - Assumption of independence among index elements (consumption, life expectancy, leisure) is strong; correlations likely exist and could alter results. - Valuation of global carbon: alternative approaches could quantify welfare effects of global carbon damages on particular countries rather than charging countries their per-capita share; adjusted net savings data exclude CO2 emissions damage in this paper’s ANS gap operationalization to avoid double counting. - Potential extensions: include gender equity, education and health, governance, social cohesion, and capital to broaden welfare measurement and to better benchmark SDG progress. ### Conclusion (summary) - The Jones and Klenow welfare index updated to 2014 yields results very similar to the original: strong correlation with GDP per capita but significant deviations largely driven by life expectancy; consumption, leisure and inequality matter in particular cases. - Between 2007 and 2014 both welfare and income converged toward advanced-country levels, with population-weighted convergence of welfare by 2.5 percentage points and income by 4.6 percentage points toward the US level. Life-expectancy improvements were the main driver of welfare gains. - Environmental adjustments (DALYs, GHG costs at US$30/ton, ANS negative gaps) reveal that: - DALY-related life-expectancy losses have small welfare effects overall. - GHG-cost-adjusted consumption reduces welfare notably in countries with high emissions per unit of consumption (up to several percentage points of US welfare level). - Unsustainable consumption (negative ANS) yields material welfare losses for affected countries. - The index can be useful for policy benchmarking (including SDGs) and for assessing welfare implications of reforms, but further refinements and additional dimensions remain important avenues for future work. *Source: IMF Working Paper wp17271 (sections and tables as provided in the supplied content unit).* ### 1. Welfare vs. Income in 2014 .......................................................................................... ### 1. Welfare vs. Income in 2014 ### Introduction and motivation - GDP (or GNI) per capita has been the primary metric for living standard comparisons but omits dimensions that affect welfare, including: - the distribution of income and wealth, - household production, - use and destruction of the natural environment, - inequality in economic opportunity, - governance, security, health and longevity. - Alternative summary measures exist (HDI, IDI, Multidimensional Poverty Index) but have been criticized for lack of theoretical foundations (Fleurbaey, 2009; Ravallion, 2012). - Jones and Klenow (2010, 2016) propose a theoretically grounded summary measure of welfare based on consumption equivalents that combine consumption, leisure, inequality and mortality using expected utility (in the tradition of Lucas, 1987). ### Methodology and extensions used in this chapter - Updated index calculation for the years 2007 and 2014 using a broader set of countries (Jones and Klenow originally for 1980 and 2007). - The Jones and Klenow approach: compute a consumption equivalent—what proportion of U.S. consumption, given U.S. valuations of leisure, mortality and inequality, yields the same expected lifetime utility as in another country. - Extension introduced in this paper: - Incorporate environmental externalities and sustainable use of natural resources via a national-level public environmental “bad” (pollution). - The public bad reduces representative household welfare through three channels: 1. increasing mortality/morbidity due to environmental health effects, lowering expected consumption, 2. reducing household consumption in proportion to a tax on a global environmental externality (greenhouse gas emissions, GHG), 3. lowering consumption in proportion to the level of unsustainable exploitation of natural resources. ### Coverage - Welfare index calculations presented for 152 countries in 2014 to examine patterns of welfare across countries. ### Key empirical findings for 2014 - Welfare is highly correlated with GDP per capita, but significant deviations exist, mostly related to differences in life expectancy. - Consumption, leisure and inequality also affect the index in particular cases. - For Asian countries: - Welfare is generally related to differences in life expectancy. - Consumption, leisure and inequality have negative effects on the index because Asian households tend to consume less, work more, and face higher inequality than in the U.S. ### Growth and convergence, 2007–2014 - Analysis of changes between 2007 and 2014—a period reflecting the impact of the global financial crisis—allows assessment of convergence in the welfare index and income. - Aggregate and regional patterns observed: - Income per capita converged across countries from 2007 to 2014. - Welfare converged even more than income over the same period. - Income and welfare followed the same regional pattern, but in aggregate welfare grew more quickly than income, mainly due to gains in life expectancy. - Crisis countries (in Western Europe and North America) experienced much slower income growth than the rest of the world. - Across most regions, welfare growth outpaced income growth because of life expectancy gains. - Exception: Asia—welfare growth lagged rapid income growth because the income share of current consumption did not grow as rapidly. ### Environmental effects on welfare - Accounting for environmental effects shows heterogenous impacts across countries: - Loss of life years from environmental diseases has only a very small effect on the index, concentrated in poor countries where life expectancy is already very low. - Accounting for the effects of GHG emissions has a large effect on welfare in some countries, indicating reliance on environmental resource use. - The countries most negatively affected by GHG-related welfare losses are not necessarily those with low rankings on some environmental protection indexes (e.g., Environmental Protection Index by Hsu et al., 2016), but overlap with countries identified by the World Bank (WB 2011) as consuming environmental resources in an unsustainable fashion according to the adjusted net savings (ANS) metric. ### Synthesis of implications - The consumption-equivalent welfare measure provides a theoretically consistent way to compare well-being across countries and over time, capturing effects of consumption, leisure, inequality, mortality and (with the paper’s extension) environmental externalities. - Differences in life expectancy are a dominant source of divergence between welfare and GDP per capita. - Environmental externalities, especially GHG emissions and unsustainable resource use measured via ANS, can substantially alter welfare rankings for certain countries. - The period 2007–2014 featured faster welfare convergence than income convergence, driven largely by life expectancy improvements. *Source: wp17271 - 1. Welfare vs. Income in 2014 (chapter content, IMF working paper).* ### Section 3 looks at the results of the welfare calculations over time. We measure changes in ### wp17271 - Section 3 looks at the results of the welfare calculations over time. We measure changes in ### Welfare and GDP in 2014 (Jones and Klenow macro update) - Method: macro formulation of Jones and Klenow (2016) implemented for 2007, 2012 and 2014 over a sample of 152 countries using Penn World Tables version 9.0 and World Bank data. - Key formulae and decomposition: lifetime utility and the welfare index ߣ௜ are based on expected lifetime utility (equations (1)–(3)), decomposed into life expectancy, consumption, leisure, and inequality contributions. - Calibration note: intercept term calibrated so value of remaining life of a 40-year-old in the US equals US$6 million (yields about 5 when normalized by US aggregate consumption per capita). ### Findings: Welfare vs Income in 2014 - Correlation: welfare and GDP per capita are highly correlated (correlation coefficient of 0.95). - Dispersion: mean absolute deviation from unity of the ratio (lambda to GDP per capita) is about 15 percent. - Population-weighted averages (2014): overall population weighted average welfare is 22.6 percent of the US level; weighted average per capita income is 28.0 percent of the US level. - Dominant factors: - Life expectancy is the dominant driver of deviations between welfare and income: high-income countries often have higher welfare relative to income due to higher life expectancy, more leisure and less inequality. - Poorer countries typically show welfare below relative income, driven mainly by lower life expectancy and higher inequality. - Consumption can dominate in specific cases (poorer countries and small island economies that consume beyond income due to grants). - Regional snapshot (population-weighted, 2014): - Europe aggregate: welfare 58.0 percent of US level; per capita income 56.0 percent (log-ratio decomposition shows small positive life-expectancy contribution). - Western Europe: welfare 85.2 percent; income 73.8 percent (positive life expectancy, leisure, lower inequality contributions). - Sub-Saharan Africa: welfare 4.2 percent; income 7.8 percent (large negative life-expectancy and consumption contributions). - Asian pattern: high-income Asian countries show positive life-expectancy contributions; many middle- and low-income Asian countries (including China and Indonesia) show large negative contribution from current consumption and leisure due to high savings/investment and longer hours worked. ### Convergence 2007–2014 (levels and changes) - Level changes (Table 2): population-weighted results indicate gradual convergence in both income and welfare toward the US level, but convergence of income about twice as fast as welfare. - Population-weighted changes between 2007 and 2014: - Welfare convergence: 2.5 percentage points toward the US level. - Income convergence: 4.6 percentage points toward the US level. - Decomposition: improvements in life expectancy contributed strongly to welfare convergence; improvements in consumption, leisure and inequality were generally insufficient to match income convergence. ### Growth 2007–2014 (rates and decomposition) - Aggregate growth (population-weighted average annual rates, Table 3): - Average, unweighted: Welfare 3.92; Income 2.92 (columns show average annual growth rates). - Average, pop-weighted: Welfare 4.87; Income 4.64. - Aggregate statement: both welfare and (PPP-adjusted) per capita income grew rapidly at between 4 ½ and 5 percent per annum over the period (driven by large weight of fast-growing countries such as China and Indonesia). - Regional growth highlights (Table 3 and text): - Europe: Welfare 4.18; Income 2.47 (population-weighted average annual rates). - Western Europe: Welfare 2.33; Income 0.88. - Eastern Europe: Welfare 6.16; Income 4.16. - North America: Welfare 1.33; Income 0.42. - Latin America: Welfare 4.48; Income 3.55. - Asia: Welfare 5.78; Income 6.21 (difference driven by negative contribution of consumption share and worsening inequality). - Sub-Saharan Africa: Welfare 3.77; Income 3.60. - Country examples (Table 4, average annual growth rates, decomposition of difference): - United States: Welfare 1.12; Income 0.19 (life expectancy and leisure positive contributors). - China: Welfare 4.86; Income 6.54 (negative contribution from C/Y: -1.68). - Indonesia: Welfare 6.01; Income 9.52 (C/Y contribution -3.51; inequality contribution -1.27). - Japan: Welfare 1.92; Income -0.35 (consumption share contribution large positive). - Hong Kong: Welfare 3.64; Income -1.99 (C/Y contribution 5.63). - Summary: gains in income were largely matched by gains in welfare, with life expectancy improvements the main contribution to welfare growth; consumption gains were small or negative on average; leisure and inequality changed little. ### Extension: Environmental externalities, sustainable consumption (2012 data) - Implementation approach (2012): - Introduce DALYs (age-standardized disability adjusted life years) attributable to environmental factors to disaggregate life expectancy into adjusted life expectancy and years of life lost to environmental pollution. - Introduce a CO2 (GHG) emissions welfare cost: set social cost of each ton at US$30, converted to PPP constant dollars; define ݎ(݀) = log(1 - ߬), with ߬ = (social cost of CO2 emissions in country i per unit of consumption). - Adjust consumption for sustainability using Adjusted Net Savings (ANS) gap: when ANS negative, consumption is reduced by the size of the negative net savings gap. - DALYs / life-expectancy effects (Table 5): - Countries with highest DALYs relative to the US (largest welfare loss) are mostly poor African countries (Sierra Leone, Chad, Swaziland, Cote d’Ivoire). - Overall magnitude: effect of years of life lost to environmental pollution is small — on the order of one half of one percent of flow utility for the most affected countries (DALYs around a third of a year per capita in the worst cases). - Welfare loss from global GHG emissions (Table 6): - Valuing GHG emissions at US$30/ton and charging as a consumption-equivalent tax reduces recorded welfare in countries with large emissions per unit of consumption. - Examples of most negatively affected (main countries negatively affected): Bolivia, Lao PDR, Zambia are highlighted as having large emissions per unit of consumption (linked to land use changes and forestry). Inclusion of GHG effects reduces recorded welfare by 5 percent, 3.3 percent, and 1.7 percentage points of the US level respectively for these countries (text statement). - Mechanism: two effects — (i) indirect life-expectancy effect (tax reduces flow consumption and thus reduces consumption-weighted life-expectancy shortfall), and (ii) direct welfare loss from the tax on GHG emissions (usually dominates). - Some countries are positively affected (Table 7) because their emissions per unit of consumption are lower than the US (examples include advanced European countries, Japan, Hong Kong, Costa Rica); positive effects range up to about 0.01 (one hundredth) to 0.10 (one tenth) of a percentage point of US welfare level for some countries in Table 7. - Adjusted Net Savings (ANS) / sustainability (Table 8 and Table 9): - Countries with negative ANS in 2012 (ANS gap, percent of consumption) include Guinea (consumption per capita 1.85; ANS gap -44.68), Oman (35.24; -26.10), Liberia (1.93; -21.07), Togo (2.12; -20.48), Greece (50.61; -7.43), Lebanon (33.00; -5.62), Laos (8.23; -4.58), Madagascar (2.83; -3.68), Belize (16.48; -3.53), Cameroon (5.42; -2.17), Malawi (2.68; -1.18), Tunisia (20.67; -0.82), Barbados (30.93; -0.35), Portugal (52.30; -0.21). - Welfare effect: inclusion of ANS gap reduces consumption (to be sustainable), producing two counteracting effects (reduced consumption-weighted life-expectancy shortfall vs. direct reduction in consumption). The direct consumption effect typically dominates. - Example magnitudes: Guinea’s welfare index declines proportionally by 35 percent (from 2.5 to 1.6 percentage points of the US level); Oman declines by 25 percent (11 percentage points of the US level); Portugal declines by 0.6 percent (0.4 percentage points of the US level). (These proportional examples are reported in the text.) - Overall environmental findings: - DALY-adjusted life-expectancy effects on welfare are small in magnitude. - GHG emissions valued at US$30/ton can reduce welfare materially where emissions per unit of consumption are high (declines up to several percentage points of US welfare level for the worst-affected countries). - Negative ANS (unsustainable consumption) causes nontrivial welfare declines for affected countries. ### Policy-relevant implications and suggested extensions - Policy implication: rebalancing growth toward domestic consumption (away from investment/exports) in countries with low C/Y can raise welfare (explicitly highlighted for China, Indonesia, and several ASEAN countries). - Environmental policy implication: accounting for global GHG costs and sustainability (ANS) alters welfare rankings and highlights the welfare importance of natural-resource-intensive consumption in some countries. - Data and methodological caveats / suggested extensions: - Need to adjust labor input measures to take into account involuntary unemployment (especially in crisis countries) and informal labor in lower-income countries — current Penn Tables hours-worked data do not capture these effects fully. - Assumption of independence among index elements (consumption, life expectancy, leisure) is strong; correlations likely exist and could alter results. - Valuation of global carbon: alternative approaches could quantify welfare effects of global carbon damages on particular countries rather than charging countries their per-capita share; adjusted net savings data exclude CO2 emissions damage in this paper’s ANS gap operationalization to avoid double counting. - Potential extensions: include gender equity, education and health, governance, social cohesion, and capital to broaden welfare measurement and to better benchmark SDG progress. ### Conclusion (summary) - The Jones and Klenow welfare index updated to 2014 yields results very similar to the original: strong correlation with GDP per capita but significant deviations largely driven by life expectancy; consumption, leisure and inequality matter in particular cases. - Between 2007 and 2014 both welfare and income converged toward advanced-country levels, with population-weighted convergence of welfare by 2.5 percentage points and income by 4.6 percentage points toward the US level. Life-expectancy improvements were the main driver of welfare gains. - Environmental adjustments (DALYs, GHG costs at US$30/ton, ANS negative gaps) reveal that: - DALY-related life-expectancy losses have small welfare effects overall. - GHG-cost-adjusted consumption reduces welfare notably in countries with high emissions per unit of consumption (up to several percentage points of US welfare level). - Unsustainable consumption (negative ANS) yields material welfare losses for affected countries. - The index can be useful for policy benchmarking (including SDGs) and for assessing welfare implications of reforms, but further refinements and additional dimensions remain important avenues for future work. *Source: IMF Working Paper wp17271 (sections and tables as provided in the supplied content unit).* ### References ### wp17271 - References ### Multidimensional poverty, welfare, and social progress - Alkire, Sabina and James Foster, 2011. “Counting and Multidimensional Poverty Measurement.” Journal of Public Economics, Vol. 95 issue 7-8, 476-487. - Fleurbaey, Marc, 2009. “Beyond GDP: The Quest for a Measure of Social Welfare.” Journal of Economic Literature, Vol 47, No. 4. 1029-1075. - Jones, Charles I. and Peter J. 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Independent Review of UK Economic Statistics: Final Report. Commissioned by the Chancellor of the Exchequer and the Minister for the Cabinet Office. U.K. Government. - Lucas, Robert E. Jr., 1987. Models of Business Cycles. New York, Basil Blackwell. - Solow, Robert M., 1974. “The Economics of Resources or the Resources of Economics.” American Economic Review, Vol. 64, No. 2, Papers and Proceedings of the Eighty-sixth Annual meeting of the American Economic Association (May, 1974), 1-14. - World Economic Forum, 2017. The Global Competitiveness Report, 2017-2018 (Geneva). ### Gender, labor, inequality, and growth - Elborgh-Woytek, Katrin et al. 2013. Women, Work, and the Economy: Macroeconomic Gains from Gender Equity. IMF Staff Discussion Note, SDN/13/10. Washington DC. - Kochhar, Kalpana, Sonali Jain-Chandra and Monique Newiak, editors, 2017. Women, Work and Economic Growth; Leveling the Playing Field. International Monetary Fund, Washington DC. - Ostry, Jonathan David, Andrew Berg and Charalambos G. Tsangarides, 2014. Redistribution, Inequality and Growth. IMF Staff Discussion Note, SDN/14/02. Washington DC. *Source: wp17271 - References (pdf).* --- _Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17271.pdf_